Integrated Metabolic and Inflammatory Clustering Reveals Distinct Risk Profiles for Digestive Diseases

Abstract

Emerging research highlights the complex relationship between metabolic dysfunction and chronic low-grade inflammation, which disrupts gut homeostasis and drives disease progression. However, most current studies evaluate metabolic and inflammatory markers separately, relying on basic indicators such as body mass index (BMI) or individual biomarkers. In this study, a scalable clustering framework is developed to integrate six clinical parameters in 398 432 participants from the UK Biobank, identifying four distinct metabolic-inflammatory subtypes. Cox proportional hazards models demonstrate significant associations between these subtypes and digestive disease risk. Using 251 plasma metabolites and elastic net regression, cluster-associated metabolite signatures are identified. Mediation analyses indicate that metabolic signatures mediate the association between clusters and digestive disease risk. Machine learning algorithms are applied to construct disease-specific metabolic risk scores, achieving C-indices above 0.70 for ten digestive disease endpoints. Explainable machine learning approaches further identify both shared and disease-specific predictors, with glycoprotein acetyls, valine, tyrosine, and fatty acids emerging as key risk indicators. This integrative approach provides a comprehensive framework for digestive disease risk assessment and offers novel insights into the metabolic mechanisms underlying disease susceptibility.

Keywords: cluster analysis; digestive diseases; machine learning; metabolomics.